ElasticSearch: from basics to advanced search system, recommendation module, php/python/java integration at real example
InstructorSergii DemianchukEditorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This diagram illustrates the lab environment setup using Docker across Ubuntu, Windows, and Mac OS platforms to run ElasticSearch in single mode or a 3-node cluster.
This screenshot displays a global map interface featuring color-coded data points and numerical values distributed across continents to visualize geographic information.
This slide displays a collection of technology logos representing the multi-language integration covered in the course, featuring PHP, Python, Java, and their respective frameworks.
This diagram illustrates a zero-downtime reindexing strategy where an application writes to both old and new indexes through an alias, allowing seamless migration using a reindex API.
Free
The curriculum provides structured implementations for integrating ElasticSearch with PHP/Symfony, Python/Flask, and Java/Spring Boot environments.
Instruction covers cluster internals, shard performance, and deployment using Terraform and Ansible.
Learner signals describe the lectures as clear, concise, and easy to digest at a good pace.
The curriculum includes specific lessons for upgrading environments to newer versions of PHP, Python, and Java alongside ElasticSearch 8.
The curriculum aligns with the target of software engineers through multi-language integration and production topics.